An AI agent can have a powerful model, the right tools and a carefully designed system prompt, yet still make the wrong decision because it was given the wrong context.

As agents become more capable, context is becoming an engineering problem of its own. Too little context and the agent misses critical information. Too much and important signals disappear in the noise. Stale memory, conflicting instructions, irrelevant retrievals and poorly scoped tool results can all push an agent toward a confident but incorrect action.

This session explores context engineering as an architectural discipline for building reliable AI agents. We will look at how instructions, retrieved information, conversation state, memory and tool outputs flow through an agent, where context becomes noisy or misleading, and how to deliberately control what the model sees.

We will examine practical patterns for context selection, retrieval, memory and context compression, along with the trade-offs between relevance, reliability, latency and cost.

Attendees will leave with a practical framework for designing agents that receive the right information at the right time, instead of simply giving increasingly capable models increasingly large amounts of information.